Short-term Traffic Flow Prediction based on Ensemble Real-time Sequential Extreme Learning Machine under Non-stationary Condition

摘要

Short-term traffic flow forecasting has been a crucial component in the area of intelligent transportation systems (ITS), which plays a significant role in operating traffic management systems and dynamic traffic assignment effectively as well as proactively. In this paper, a novel short-term traffic flow prediction method called Ensemble Real-time Sequential Extreme Learning Machine (ERS-ELM) with simplified single layer feed-forward networks (SLFN) structure under freeway peak traffic condition and non-stationary condition is proposed. By quickly training historical data and incrementally updating model with new arrived data, ERE-ELM has the characteristics of less training time consumption and high prediction accuracy. Ensemble mechanism is also used to improve stability and robustness. Experiment results show that average mean absolute percentage error (MAPE), test root mean square error (RMSE) as well as training time consumption of proposed method is superior to classical Wave-NN, MLP-NN and ELM methods.